SOTAVerified

Extractive Text Summarization

Given a document, selecting a subset of the words or sentences which best represents a summary of the document.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 95 papers

TitleStatusHype
Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization—0
Summary Level Training of Sentence Rewriting for Abstractive Summarization—0
Topic Modeling Based Extractive Text Summarization—0
Towards a Reliable and Robust Methodology for Crowd-Based Subjective Quality Assessment of Query-Based Extractive Text Summarization—0
Towards automatic extractive text summarization of A-133 Single Audit reports with machine learning—0
Towards Supervised Extractive Text Summarization via RNN-based Sequence Classification—0
Twitter Topic Summarization by Ranking Tweets using Social Influence and Content Quality—0
Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization—0
Using Document Summarization Techniques for Speech Data Subset Selection—0
Using Statistical and Semantic Models for Multi-Document Summarization—0
Using Supervised Bigram-based ILP for Extractive Summarization—0
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information—0
Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN—0
Abstractive Text Summarization for Sanskrit Prose: A Study of Methods and Approaches—0
A Dynamic Programming Algorithm for Tree Trimming-based Text Summarization—0
A Hierarchical Structured Self-Attentive Model for Extractive Document Summarization (HSSAS)—0
A Hybrid PSO-GA for Extractive Text Summarization—0
A New Persian Text Summarization Approach Based on Natural Language Processing and Graph Similarity—0
A Novel System for Extractive Clinical Note Summarization using EHR Data—0
A New Sentence Extraction Strategy for Unsupervised Extractive Summarization Methods—0
A study of semantic augmentation of word embeddings for extractive summarization—0
A topic-based sentence representation for extractive text summarization—0
At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization—0
BoWLer: A neural approach to extractive text summarization—0
Classify or Select: Neural Architectures for Extractive Document Summarization—0
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All datasetsCNN / Daily MailDebateSumGovReportDUC 2004DUC 2004 Task 1

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HAHSumROUGE-144.68—Unverified
2Scaled-MatchSumROUGE-144.51—Unverified
3MatchSumROUGE-144.41—Unverified
4A2SummROUGE-144.11—Unverified
5NeRoBERTaROUGE-143.86—Unverified
6BERT-ext + RLROUGE-142.76—Unverified
7PNBERTROUGE-142.69—Unverified
8HIBERTROUGE-142.37—Unverified
9HERROUGE-142.3—Unverified
10NeuSUMROUGE-141.59—Unverified
#ModelMetricClaimedVerifiedStatus
1Longformer-BaseROUGE-L57.21—Unverified
2GPT2-MediumROUGE-L53.23—Unverified
3BERT-LargeROUGE-L49.98—Unverified
#ModelMetricClaimedVerifiedStatus
1MemSum (extractive)Avg. Test Rouge159.43—Unverified
2HEPOSAvg. Test Rouge156.86—Unverified
#ModelMetricClaimedVerifiedStatus
1Pre-training-meets-Clustering-A-Hybrid-Extractive-Multi-Document-Summarization-ModelTest ROGUE-134.01—Unverified
#ModelMetricClaimedVerifiedStatus
1AbsROUGE-126.55—Unverified